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SIGMOD2009 Overview

SIGMOD2009 Overview. Web group Li Yukun. Outline. Overview SIGMOD2009 Overview two selected papers Optimizing Complex Extraction Programs over Evolving Text Data Exploiting Context Analysis for Combining Multiple Entity Resolution Systems. Section of SIGMOD2009.

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SIGMOD2009 Overview

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  1. SIGMOD2009 Overview Web group Li Yukun

  2. Outline • Overview SIGMOD2009 • Overview two selected papers • Optimizing Complex Extraction Programs over Evolving Text Data • Exploiting Context Analysis for Combining Multiple Entity Resolution Systems

  3. Section of SIGMOD2009 • Research Session 1: Security I • Research Session 2: Databases on Modern Hardware • Research Session 3: Information Extraction • Research Session 4: Security II • Research Session 5: Large-Scale Data Analysis • Research Session 6: Entity Resolution • Research Session 7: Testing and Security • Research Session 8: Column Stores • Research Session 9: Data on the Web • Research Session 10: Probabilistic Databases I • Research Session 11: Database Optimization • Research Session 12: Probabilistic Databases II • Research Session 13: Skyline Query Processing • Research Session 14: Understanding Data and Queries • Research Session 15: Nearest Neighbor Search • Research Session 16: Query Processing on Semi-structured Data • Research Session 17: Data Integration • Research Session 18: Keyword Search • Research Session 19: Semi-structured Data Management • Research Session 20: Data Management Pearls • Research Session 21: Indexing

  4. SIGMOD keynote talks • Enterprise Applications - OLTP and OLAP - Share One Database ArchitectureHasso Plattner (Hasso-Plattner-Institute for IT Systems Engineering) • Transforming Data Access Through Public VisualizationFernanda B. Viegas (IBM)Martin Wattenberg (IBM) Web-based visualizations—ranging from political art projects to news stories—have reached audiences of millions. Meanwhile, new initiatives in government, aimed at all citizens, point to an era of increased transparency. a "living laboratory" web site where people may upload their own data, create interactive visualizations, and carry on conversations. Political discussions, citizen activism, religious discussions, game playing, and educational exchanges all happen on the site. To further support these scenarios, and the users they represent, will require continued innovation in data presentation and interaction.

  5. SIGMOD INVITED SESSIONS • Special Invited Session on Human-Computer Interaction with InformationDesign for InteractionDaniel Tunkelang (Endeca)Voyagers and Voyeurs: Supporting Social Data AnalysisJeffrey Heer (Stanford University)Augmented Social CognitionEd H. Chi (PARC) • Special Invited Session on Systems Research and Information ManagementStorage Class Memory: Technology, Systems and ApplicationsRichard F. Freitas (IBM)Distributed Data-Parallel Computing Using a High-Level Programming LanguageMichael Isard (Microsoft Research)Yuan Yu (Microsoft Research)

  6. SIGMOD TUTORIALS • Large-Scale Uncertainty Management Systems: Learning and Exploiting Your Data • FPGA: What's in it for a Database? • Keyword Search on Structured and Semi-Structured Data • Database Research in Computer Games • Anonymized Data: Generation, Models, Usage

  7. Summary • Hot words • Probabilistic,Semi-structure, Security, Search&Query, Extraction&resolution • User Interaction

  8. DataSpace Framework

  9. Future work on DataSpace • Managing Entity and association • Entity Identify and Resolution • Data extraction and cleaning • Pay-as-you-go integration • Uncertain data mapping • Update of entity and association • Query&Search in dataspace • Keyword search • Approximate query • Facet-based search in dataspace

  10. Selected readings • Data integration • Top-K Generation of Integrated Schemas Based on Directed and Weighted Correspondences • Core Schema Mappings • Entity Resolution • Exploiting Context Analysis for Combining Multiple Entity Resolution Systems • Entity Resolution with Iterative Blocking • A Grammar-based Entity Representation Framework for Data Cleaning • Data on the Web • Optimizing Complex Extraction Programs over Evolving Text Data • Robust Web Extraction: An Approach Based on a Probabilistic Tree-Edit Model • Combining Keyword Search and Forms for Ad Hoc Querying of Databases • Indexing • A Revised R*-tree in Comparison with Related Index Structures • Understanding Data and Queries • Why Not? • Query by Output • Detecting and Resolving Unsound Workflow Views for Correct Provenance Analysis • Query processing on Semi-structured data • Scalable Join Processing on Very Large RDF Graphs

  11. Outline • Overview SIGMOD2009 • Two selected papers • Optimizing Complex Extraction Programs over Evolving Text Data • Exploiting Context Analysis for Combining Multiple Entity Resolution Systems

  12. Paper 1

  13. Introduction • Motivation • Traditional IE method: Static • Practical conditions: Dynamic corpus • DBlife(10000+URLs,120+MB corpus snapshot.) • Enterprise Intranet • Problem • How to efficiently extract information based on Dynamic corpora

  14. Problem Definition • Concepts • Data pages, Extractors, Mentions • An extractor E:p→R(a1,a2,…,an) extracts mentions of relation R from page p. A mention of R is a tuple(m1,m2,…,mn,)such that mi is either a mention of attribute ai or nil. • Examples • Assumptions • Extract mentions from each single data pages

  15. Methods • Concepts • Extractor scope • Let s.start and s.end be the start and end character positions of a string s in a page p. We say an extractor E has scope α iff for any mention m = (m1, . . . ,mn) produced by E, (maxi mi.end − mini mi.start) < α, where mi.start and mi.end are the start and end character positions of attribute mention mi in page p. • Extractor Context • The β-context of mention m in page p is the string p[(m.start−β)..(m.end+ β)], i.e., the string of m being extended on both sides by β characters. We say extractor E has context β iff for any m and p′ obtained by perturbing the text of p outside the β- context of m, applying E to p′ still produces m as a mention. • Clallenges • Matchers (Find overlaping)

  16. Solutions • CAPTURING IE RESULTS • Level of Reuse: • IE Results to Capture: • Storing Captured IE Results: • REUSING CAPTURED IE RESULTS • Scope of Mention Reuse • Overall Processing Algorithm • Identifying Reuse with Matchers • SELECTING A GOOD IE PLAN • Searching for Good Plans • Cost Model

  17. Evaluation(DataSet)

  18. Experimental Results

  19. Paper 2

  20. Introduction Jone Smith J. Smith John.Smith J.Smith • What is entity resolution • to identify and group references that co-refer, that is, refer to the same entity. • Motivation • New data characters: • Examples • The output • a clustering of references, where each cluster is supposed to represent one distinct entity.

  21. Problem definition • Entity Resolution • ER problem has been studied in several research areas under many names such as coreference resolution, deduplication, object uncertainty,record linkage, reference reconciliation, etc. In the past, a wide variety of techniques have been developed for ER problem. • Methods • Similarity (metrics, textual, attributes, and etc.) • Blocking • Voting • Problem • Pay little attention to context feature

  22. Problem Definition • To identify co-offer relationship between two mentions

  23. Context-based framework • Context features • Effectiveness • Generality • Number of clusters • Overview of the approaches • Meta-level Classification • Context-extended classification • Context-weighted Classification • Creating final clusters

  24. Experiments • Web domain • Data set by WWW05[Bekkerman, and etc.] • Contain web pages of 12 different persons • Created by searching web using Google • RealPub domain • 11682 publications • 14590 authors • 3084 departments • 1494 organizations

  25. Experimental results on Web domain

  26. Summary • How to manage uncertainty data, and unstructured data are becoming a hot topic • It is also important problem of DataSpace • Based on it, to select promising topics.

  27. Thanks

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